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Open AccessArticle

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

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DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, D-28359 Bremen, Germany
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Robotics Group, Department of Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, D-28359 Bremen, Germany
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(7), 1552; https://doi.org/10.3390/s17071552
Received: 7 April 2017 / Revised: 19 June 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision. View Full-Text
Keywords: brain-computer interfaces; mobile computing; embedded systems; fpgas; neuromuscular rehabilitation; movement prediction; embedded brain reading brain-computer interfaces; mobile computing; embedded systems; fpgas; neuromuscular rehabilitation; movement prediction; embedded brain reading
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Wöhrle, H.; Tabie, M.; Kim, S.K.; Kirchner, F.; Kirchner, E.A. A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction. Sensors 2017, 17, 1552.

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